Minor Re-shuffling

Author: animikhaich

CHANGELOG.md

@@ -4,8 +4,16 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## Unreleased
 
-## Version 0.1.0 Beta - 2021-04-06
+### Modified
+- Re-shuffling:
+  - `utils/data_loader.py` -> `core/data_loader.py`
+  - `utils/model.py` -> `core/model.py`
+- Moved Custom Callbacks to new file: `utils/add_ons.py`
+
+
+## [Version 0.1.0 Beta] - 2021-04-06
 ### Added
 - Dockerfile
 - Launch Script (Dependency: [GNU Parallel](https://www.gnu.org/software/parallel/))
@@ -80,3 +88,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - Adamax
   - Nadam
   - FTRL
+
+
+[Version 0.1.0 Beta]: https://github.com/animikhaich/Zero-Code-TF-Classifier/releases/tag/v0.1-beta

utils/data_loader.py renamed to core/data_loader.py

@@ -12,14 +12,11 @@
 os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
 
-from utils.data_loader import ImageClassificationDataLoader
-from utils.model import ImageClassifier
-from threading import Thread
+from core.data_loader import ImageClassificationDataLoader
+from core.model import ImageClassifier
+from utils.add_ons import CustomCallback
 import tensorflow as tf
 import streamlit as st
-import numpy as np
-import pandas as pd
-import plotly.graph_objs as go
 
 # TODO: Add Support For Live Training Graphs (on_train_batch_end) without slowing down the Training Process
 # TODO: Add Supoort For EfficientNet - Fix Data Loader Input to be Un-Normalized Images
@@ -104,157 +101,6 @@
 st.title("Zero Code Tensorflow Classifier Trainer")
 
 
-class CustomCallback(tf.keras.callbacks.Callback):
-    """
-    CustomCallback Keras Callback to Send Updates to Streamlit Dashboard
-
-    - Inherits from tf.keras.callbacks.Callback class
-    - Sends Live Updates to the Dashboard
-    - Allows Plotting Live Loss and Accuracy Curves
-    - Allows Updating of Progress bar to track batch progress
-    - Live plot only support Epoch Loss & Accuracy to improve training speed
-    """
-
-    def __init__(self, num_steps):
-        """
-        __init__
-
-        Value Initializations
-
-        Args:
-            num_steps (int): Total Number of Steps per Epoch
-        """
-        self.num_steps = num_steps
-
-        # Constants (TODO: Need to Optimize)
-        self.train_losses = []
-        self.val_losses = []
-        self.train_accuracies = []
-        self.val_accuracies = []
-
-        # Progress
-        self.epoch_text = st.empty()
-        self.batch_progress = st.progress(0)
-        self.status_text = st.empty()
-
-        # Charts
-        self.loss_chart = st.empty()
-        self.accuracy_chart = st.empty()
-
-    def update_graph(self, placeholder, items, title, xaxis, yaxis):
-        """
-        update_graph Function to Update the plot.ly graphs on Streamlit
-
-        - Updates the Graphs Whenever called with the passed values
-        - Only supports Line plots for now
-
-        Args:
-            placeholder (st.empty()): streamlit placeholder object
-            items (dict): Containing Name of the plot and values
-            title (str): Title of the Plot
-            xaxis (str): X-Axis Label
-            yaxis (str): Y-Axis Label
-        """
-        fig = go.Figure()
-        for key in items.keys():
-            fig.add_trace(
-                go.Scatter(
-                    y=items[key],
-                    mode="lines+markers",
-                    name=key,
-                )
-            )
-        fig.update_layout(title=title, xaxis_title=xaxis, yaxis_title=yaxis)
-        placeholder.write(fig)
-
-    def on_train_batch_end(self, batch, logs=None):
-        """
-        on_train_batch_end Update Progress Bar
-
-        At the end of each Training Batch, Update the progress bar
-
-        Args:
-            batch (int): Current batch number
-            logs (dict, optional): Training Metrics. Defaults to None.
-        """
-        self.batch_progress.progress(batch / self.num_steps)
-
-    def on_epoch_begin(self, epoch, logs=None):
-        """
-        on_epoch_begin
-
-        Update the Dashboard on the Current Epoch Number
-
-        Args:
-            batch (int): Current batch number
-            logs (dict, optional): Training Metrics. Defaults to None.
-        """
-        self.epoch_text.text(f"Epoch: {epoch + 1}")
-
-    def on_train_begin(self, logs=None):
-        """
-        on_train_begin
-
-        Status Update for the Dashboard with a message that training has started
-
-        Args:
-            batch (int): Current batch number
-            logs (dict, optional): Training Metrics. Defaults to None.
-        """
-        self.status_text.info(
-            "Training Started! Live Graphs will be shown on the completion of the First Epoch."
-        )
-
-    def on_train_end(self, logs=None):
-        """
-        on_train_end
-
-        Status Update for the Dashboard with a message that training has ended
-
-        Args:
-            batch (int): Current batch number
-            logs (dict, optional): Training Metrics. Defaults to None.
-        """
-        self.status_text.success(
-            f"Training Completed! Final Validation Accuracy: {logs['val_categorical_accuracy']*100:.2f}%"
-        )
-        st.balloons()
-
-    def on_epoch_end(self, epoch, logs=None):
-        """
-        on_epoch_end
-
-        Update the Graphs with the train & val loss & accuracy curves (metrics)
-
-        Args:
-            batch (int): Current batch number
-            logs (dict, optional): Training Metrics. Defaults to None.
-        """
-        self.train_losses.append(logs["loss"])
-        self.val_losses.append(logs["val_loss"])
-        self.train_accuracies.append(logs["categorical_accuracy"])
-        self.val_accuracies.append(logs["val_categorical_accuracy"])
-
-        self.update_graph(
-            self.loss_chart,
-            {"Train Loss": self.train_losses, "Val Loss": self.val_losses},
-            "Loss Curves",
-            "Epochs",
-            "Loss",
-        )
-
-        self.update_graph(
-            self.accuracy_chart,
-            {
-                "Train Accuracy": self.train_accuracies,
-                "Val Accuracy": self.val_accuracies,
-            },
-            "Accuracy Curves",
-            "Epochs",
-            "Accuracy",
-        )
-
-
 # Sidebar Configuration Parameters
 with st.sidebar:
     st.header("Training Configuration")

utils/model.py renamed to core/model.py

@@ -0,0 +1,169 @@
+__author__ = "Animikh Aich"
+__copyright__ = "Copyright 2021, Animikh Aich"
+__credits__ = ["Animikh Aich"]
+__license__ = "MIT"
+__version__ = "0.1.0"
+__maintainer__ = "Animikh Aich"
+__email__ = "[email protected]"
+__status__ = "staging"
+
+import os
+
+os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
+import tensorflow as tf
+import streamlit as st
+import numpy as np
+import pandas as pd
+import plotly.graph_objs as go
+
+
+class CustomCallback(tf.keras.callbacks.Callback):
+    """
+    CustomCallback Keras Callback to Send Updates to Streamlit Dashboard
+
+    - Inherits from tf.keras.callbacks.Callback class
+    - Sends Live Updates to the Dashboard
+    - Allows Plotting Live Loss and Accuracy Curves
+    - Allows Updating of Progress bar to track batch progress
+    - Live plot only support Epoch Loss & Accuracy to improve training speed
+    """
+
+    def __init__(self, num_steps):
+        """
+        __init__
+
+        Value Initializations
+
+        Args:
+            num_steps (int): Total Number of Steps per Epoch
+        """
+        self.num_steps = num_steps
+
+        # Constants (TODO: Need to Optimize)
+        self.train_losses = []
+        self.val_losses = []
+        self.train_accuracies = []
+        self.val_accuracies = []
+
+        # Progress
+        self.epoch_text = st.empty()
+        self.batch_progress = st.progress(0)
+        self.status_text = st.empty()
+
+        # Charts
+        self.loss_chart = st.empty()
+        self.accuracy_chart = st.empty()
+
+    def update_graph(self, placeholder, items, title, xaxis, yaxis):
+        """
+        update_graph Function to Update the plot.ly graphs on Streamlit
+
+        - Updates the Graphs Whenever called with the passed values
+        - Only supports Line plots for now
+
+        Args:
+            placeholder (st.empty()): streamlit placeholder object
+            items (dict): Containing Name of the plot and values
+            title (str): Title of the Plot
+            xaxis (str): X-Axis Label
+            yaxis (str): Y-Axis Label
+        """
+        fig = go.Figure()
+        for key in items.keys():
+            fig.add_trace(
+                go.Scatter(
+                    y=items[key],
+                    mode="lines+markers",
+                    name=key,
+                )
+            )
+        fig.update_layout(title=title, xaxis_title=xaxis, yaxis_title=yaxis)
+        placeholder.write(fig)
+
+    def on_train_batch_end(self, batch, logs=None):
+        """
+        on_train_batch_end Update Progress Bar
+
+        At the end of each Training Batch, Update the progress bar
+
+        Args:
+            batch (int): Current batch number
+            logs (dict, optional): Training Metrics. Defaults to None.
+        """
+        self.batch_progress.progress(batch / self.num_steps)
+
+    def on_epoch_begin(self, epoch, logs=None):
+        """
+        on_epoch_begin
+
+        Update the Dashboard on the Current Epoch Number
+
+        Args:
+            batch (int): Current batch number
+            logs (dict, optional): Training Metrics. Defaults to None.
+        """
+        self.epoch_text.text(f"Epoch: {epoch + 1}")
+
+    def on_train_begin(self, logs=None):
+        """
+        on_train_begin
+
+        Status Update for the Dashboard with a message that training has started
+
+        Args:
+            batch (int): Current batch number
+            logs (dict, optional): Training Metrics. Defaults to None.
+        """
+        self.status_text.info(
+            "Training Started! Live Graphs will be shown on the completion of the First Epoch."
+        )
+
+    def on_train_end(self, logs=None):
+        """
+        on_train_end
+
+        Status Update for the Dashboard with a message that training has ended
+
+        Args:
+            batch (int): Current batch number
+            logs (dict, optional): Training Metrics. Defaults to None.
+        """
+        self.status_text.success(
+            f"Training Completed! Final Validation Accuracy: {logs['val_categorical_accuracy']*100:.2f}%"
+        )
+        st.balloons()
+
+    def on_epoch_end(self, epoch, logs=None):
+        """
+        on_epoch_end
+
+        Update the Graphs with the train & val loss & accuracy curves (metrics)
+
+        Args:
+            batch (int): Current batch number
+            logs (dict, optional): Training Metrics. Defaults to None.
+        """
+        self.train_losses.append(logs["loss"])
+        self.val_losses.append(logs["val_loss"])
+        self.train_accuracies.append(logs["categorical_accuracy"])
+        self.val_accuracies.append(logs["val_categorical_accuracy"])
+
+        self.update_graph(
+            self.loss_chart,
+            {"Train Loss": self.train_losses, "Val Loss": self.val_losses},
+            "Loss Curves",
+            "Epochs",
+            "Loss",
+        )
+
+        self.update_graph(
+            self.accuracy_chart,
+            {
+                "Train Accuracy": self.train_accuracies,
+                "Val Accuracy": self.val_accuracies,
+            },
+            "Accuracy Curves",
+            "Epochs",
+            "Accuracy",
+        )